Laser barcode decoding and diagnostic systems typically employ lower-resolution binarized data of one scan period at a time by first converting from higher-resolution scanline data to a lower-resolution binary sequence. Then, in the more advanced systems, barcode patterns are reconstructed from binarized data of multiple scan periods. For example, when using a laser scanner to read a one-dimensional (i.e., linear) barcode, there are many applications in which modules of the barcode do not line up approximately perpendicularly to the scanline. In such situations, no single scanline encompasses the entire width of the barcode, thereby leaving a portion of the modules excluded from a given sample line of the scanline data. Therefore, decoded portions of binary sequences obtained from multiple scanlines are virtually stitched together in an attempt to generate a sequence that encompasses all of the modules of the barcode. Thus, one-dimensional (i.e., signal) decoding techniques are used to decode the barcode by detecting a sequence of edge transitions within the stitched binarized sequence.
Datalogic Automation Inc. and Accu-Sort Systems, Inc. have attempted one-dimensional barcode information reconstruction by using algorithms known as, respectively, Advanced Code Reconstruction (ACR™ 4) and Data Reconstruction (DRX®). But as noted in the previous paragraph, these algorithms use binarized scanline data to identify known barcode patterns or known barcode behaviors in order to match the data across scanlines. In performing these conventional methods, information is lost before it can be processed. This loss of information reduces the effectiveness of the decoding algorithm and makes diagnostics difficult for anyone but a highly trained technician capable of interpreting the signal (i.e., waveform) information. Because these previous data processing techniques use a linear sequence of edge transitions to determine one-dimensional barcode information, they do not provide for a visual representation of the barcode that could otherwise be used for two-dimensional (i.e., image-based) diagnostic or decoding technologies.